Hyung Won Chung; Le Hou; Shayne Longpre; Barret Zoph; Yi Tay; William Fedus; Yunxuan Li; Xuezhi Wang; Mostafa Dehghani; Siddhartha Brahma; Albert Webson; Shixiang Shane Gu; Zhuyun Dai; Mirac Suzgun; Xinyun Chen; Aakanksha Chowdhery; Alex Castro-Ros; Marie Pellat; Kevin Robinson; Dasha Valter; Sharan Narang; Gaurav Mishra; Adams Yu; Vincent Zhao; Yanping Huang; Andrew Dai; Hongkun Yu; Slav Petrov; Ed H. Chi; Jeff Dean; Jacob Devlin; Adam Roberts; Denny Zhou; Quoc V. Le; Jason Wei

Abstract

Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT), and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation). For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PALM 540B by a large margin (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints, which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models.